The ecological and economic importance of forest trees is evident and their survival is necessary to provide the raw materials needed for wood and paper industries, to preserve the diversity of associated animal and plant species, to protect water and soil, and to regulate climate. Forest trees are threatened by anthropogenic factors and biotic and abiotic stresses. Various diseases, including those caused by fungal pathogens, are one of the main threats to forest trees that lead to their dieback. Genomics and transcriptomics studies using next-generation sequencing (NGS) methods can help reveal the architecture of resistance to various diseases and exploit natural genetic diversity to select elite genotypes with high resistance to diseases. In the last two decades, QTL mapping studies led to the identification of QTLs related to disease resistance traits and gene families and transcription factors involved in them, including NB-LRR, WRKY, bZIP and MYB. On the other hand, due to the limitation of recombination events in traditional QTL mapping in families derived from bi-parental crosses, genome-wide association studies (GWAS) that are based on linkage disequilibrium (LD) in unstructured populations overcame these limitations and were able to narrow down QTLs to single genes through genotyping of many individuals using high-throughput markers. Association and QTL mapping studies, by identifying markers closely linked to the target trait, are the prerequisite for marker-assisted selection (MAS) and reduce the breeding period in perennial forest trees. The genomic selection (GS) method uses the information on all markers across the whole genome, regardless of their significance for development of a predictive model for the performance of individuals in relation to a specific trait. GS studies also increase gain per unit of time and dramatically increase the speed of breeding programs. This review article is focused on the progress achieved in the field of dissecting forest tree disease resistance architecture through GWAS and QTL mapping studies. Finally, the merit of methods such as GS in accelerating forest tree breeding programs is also discussed.

Foeniculum vulgare Mill. (commonly known as fennel) is used in the pharmaceutical, cosmetic, and food industries. Fennel widely used as a digestive, carminative, galactagogue and diuretic and in treating gastrointestinal and respiratory disorders. Improving low heritability traits such as essential oil yield (EOY%) and trans-anethole yield (TAY%) of fennel by direct selection does not result in rapid gains of EOY% and TAY%. Identification of high-heritable traits and using efficient modeling methods can be a beneficial approach to overcome this limitation and help breeders select the most advantageous traits in medicinal plant breeding programs. The present study aims to compare the performance of the artificial neural network (ANN) and multilinear regression (MLR) to predict the EOY% and TAY% of fennel populations. Stepwise regression (SWR) was used to assess the effect of various input variables. Based on SWR, nine traits—number of days to 50% flowering (NDF50%), number of days to maturity (NDM), final plant height (FPH), number of internodes (NI), number of umbels (NU), seed yield per square meter (SY/m²), number of seeds per plant (NS/P), number of seeds per umbel (NS/U) and 1000-seed weight (TSW)—were chosen as input variables. The network with Sigmoid Axon transfer function and two hidden layers was selected as the final ANN model for the prediction of EOY%, and the TanhAxon function with one hidden layer was used for the prediction of TAY%. The results revealed that the ANN method could predict the EOY% and TAY% with more accuracy and efficiency (R² of EOY% = 0.929, R² of TAY% = 0.777, RMSE of EOY% = 0.544, RMSE of TAY% = 0.264, MAE of EOY% = 0.385 and MAE of TAY% = 0.352) compared with the MLR model (R² of EOY% = 0.553, R² of TAY% = 0.467, RMSE of EOY% = 0.819, RMSE of TAY% = 0.448, MAE of EOY% = 0.624 and MAE of TAY% = 0.452). Based on the sensitivity analysis, SY/m², NDF50% and NS/P were the most important traits to predict EOY% as well as SY/m², NS/U and NDM to predict of TAY%. The results demonstrate the potential of ANNs as a promising tool to predict the EOY% and TAY% of fennel, and they can be used in future fennel breeding programs.

Artemisia fragrans is an aromatic and perennial herb with green silver leaves and yellow flowers, widely distributed in Iran, Azerbaijan, Russia and Turkey. The plant and its essential oil (EO) are used in traditional medicine and have economic value. In this study, the composition, antibacterial and antioxidant activities of 16 A. fragrans essential oil samples collected from different habitats in Iran were analyzed. A total of 85 compounds were detected using GC-MS analysis, the major constituents being camphor (9.91-34.44 %), α-thujone (19.22-42.63 %) and 1,8-cineole (12.57-31.87 %). Davanone D, α-cadinol, verbenene, ortho-oci-men were detected in the analysis as the new components for this species and are first reported by us. Anti-bacterial activity and minimum inhibitory and bactericidal concentrations of EOs were tested on Escherichia coli, Klebsiella pneumonia and Proteus vulgaris (Gram-negative strains) Staphylococcus aureus, Staphylococ-cus epidermidis and Bacillus subtilis (Gram-positive strains). The EOs showed better inhibitory effects against Gram-negative strains compared to Gram-positive ones. Among the EOs, E7 (IC₅₀ = 9.1 μg/mL) population showed the highest radical scavenging capacity, which was equal to the positive control (butylated hyroxytoluene). The quantitative and qualitative variability of the studied EOs and their antibacterial and antioxidant activities may be recommended for pharmacological and cosmetic industries.

Black horehound (Ballota nigra L.) is one of the most important medicinal plants, as a rich source of health-promoting essential oils and metabolites. Salinity stress affects plant development and alters antioxidant activity and plant metabolite composition. The present research aimed to study the effect of salinity on physiological and biochemical changes and metabolites of B. nigra under greenhouse and in vitro culture conditions. The plants were treated with different concentrations of NaCl (25, 50, 75, 100 mM), and morphological characteristics of the plant were measured. The growth-related traits and soil plant analysis development (SPAD) were decreased both in vivo and in vitro. Additionally, increased salt concentration negatively affected the cell membrane integrity. The total phenolic content and flavonoids of plants growing in the greenhouse increased by 21% at 50 mM of NaCl, but the amounts decreased significantly at higher stress levels (100 mM of NaCl). Antioxidant activity was also measured. Among the NaCl treatments, the most increased DPPH scavenging activities (IC₅₀) under greenhouse and in vitro conditions were detected at mild salinity stress, but the activity significantly decreased in higher salinity treatments (i.e., 75 and 100 mM). In general, with increasing the salinity level to 75 mM, the activities of CAT and APX were significantly upregulated in both greenhouse and in vitro culture conditions. A correlation between total phenolics and flavonoids contents as well as antioxidant activity was obtained. Salinity level caused a shift in the metabolite expression. Mild salinity stress elevated the metabolites involved in anticancer and anti-inflammatory activities, such as β-ionone and caryophyllene oxide. However, the higher salt stress resulted in a significant reduction in their expression. Differential expression of metabolites to various levels of salt stress can be further exploited for the in vitro biosynthesis of metabolites.